Mortaring for linear elasticity using mixed and stabilized finite elements

The purpose of this work is to study mortar methods for linear elasticity using standard low order finite element spaces. Based on residual stabilization, we introduce a stabilized mortar method for linear elasticity and compare it to the unstabilized mixed mortar method. For simplicity, both methods use a Lagrange multiplier defined on a trace mesh inherited from one side of the interface only. We derive a quasi-optimality estimate for the stabilized method and present the stability criteria of the mixed $P_1-P_1$ approximation. Our numerical results demonstrate the stability and the convergence of the methods for tie contact problems. Moreover, the results show that the mixed method can be successfully extended to three dimensional problems

Sensitivity of calving glaciers to ice-ocean interactions under climate change: New insights from a 3D full-Stokes model

Iceberg calving accounts for between 30% and 60% of net mass loss from the Greenland Ice Sheet, which has intensified and is now the single largest contributor to global sea level rise in the cryosphere. Changes to calving rates and the dynamics of calving glaciers represent a significant uncertainty in projections of future sea level rise. A growing body of observational evidence suggests that calving glaciers respond rapidly to regional environmental change, but predictive capacity is limited by the lack of suitable models capable of simulating calving mechanisms realistically. Here, we use a 3-D full-Stokes calving model to investigate the environmental sensitivity of Store Glacier, a large outlet glacier inWest Greenland.We focus on two environmental processes: undercutting by submarine melting and buttressing by ice mélange, and our results indicate that Store Glacier is likely to be able to withstand moderate warming perturbations in which the former is increased by 50% and the latter reduced by 50 %. However, severe perturbation with a doubling of submarine melt rates or a complete loss of ice mélange destabilises the calving front in our model runs. Furthermore, our analysis reveals that stress and fracture patterns at Store’s terminus are complex and varied, primarily due to the influence of basal topography. Calving style and environmental sensitivity vary greatly, with propagation of surface crevasses significantly influencing iceberg production in the northern side, whereas basal crevasses dominate in the south. Any future retreat is likely to be initiated in the southern side by a combination of increased submarine melt rates in summer and reduced mélange strength in winter. The lateral variability, as well as the importance of rotational and bending forces at the terminus, underlines the importance of using the 3-D full-Stokes stress solution when modelling Greenland’s calving glaciers.Article is output from NERC PhD studentship (Todd) and NERC grant (Christoffersen

Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP, taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value < 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations

The coupling of solids and shells by conjugate approximations

In order to get detailed information about deformations of structures efficiently, it may be necessary to use finite element models which combine three-dimensional discretizations of solidswith approximations of two-dimensional models for shells. Here we show how the idea of conjugate approximations can be used as a means to obtain a formulation of mixed-dimensional coupling between shells and solids. Our method is consistent with respect to the principle of virtual workand does not depend on additional computational parameters, an augmentation of a potential-energy functional by introducing new unknowns, or computations over auxiliary meshes

EOF-Library: Open-source Elmer FEM and OpenFOAM coupler for electromagnetics and fluid dynamics

EOF-Library is a software that couples Elmer and OpenFOAM simulation packages. It enables efficient internal field interpolation and communication between the finite element and the finite volume frameworks. The coupling of the two packages is based on the Message Passing Interface, which results in low latency, high data bandwidth and parallel scalability. Potential applications are magnetohydrodynamics, convective cooling of electrical devices, industrial plasma physics and microwave heating. In this work we introduce the software and perform interpolation accuracy and parallel scaling tests by sending a known scalar distribution between the two codes. Keywords: Elmer, FEM, OpenFOAM, FVM, CFD, MP

Robust Development of Active Learning-based Surrogates for Induction Motor

A robust open-source cloud-based workflow is developed for finite element (FE) data generation for active learning (AL)-based surrogate modeling. Special attention is paid to making the FE solution procedure as robust and fast as possible without human intervention by, e.g., implementing special convergence criteria, reliable parallel computation, and variable timestep length. In AL, a surrogate model automatically improves itself by iteratively querying more FE data. Using AL and large datasets generated with parallelized cloud FE simulations, we develop a surrogate model to rapidly predict induction machine steady-state torque, torque ripple, total losses, and current harmonic distortion, as a function of motor frequency, voltage, and slip. Results show that AL performs better than grid sampling and on average works as well as random sampling, but with some outputs, the results vary less with AL. In addition, accurate ripple estimation requires a much larger training dataset than the other variables.</p